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read_backtest_insights

Retrieve trading strategy insights from backtest results to analyze performance and identify optimization opportunities.

Instructions

Read insights from a backtest.

Args: project_id: ID of the project containing the backtest backtest_id: ID of the backtest to read insights from start: Starting index of insights to fetch (default: 0) end: Last index of insights to fetch (default: 100, max range: 100)

Returns: Dictionary containing insights data and total count

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
backtest_idYes
startNo
endNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'read_backtest_insights' tool. It validates input parameters, authenticates with QuantConnect API, makes a POST request to 'backtests/read/insights' endpoint to fetch insights data within the specified range, and returns formatted success or error response.
    @mcp.tool()
    async def read_backtest_insights(
        project_id: int, backtest_id: str, start: int = 0, end: int = 100
    ) -> Dict[str, Any]:
        """
        Read insights from a backtest.
    
        Args:
            project_id: ID of the project containing the backtest
            backtest_id: ID of the backtest to read insights from
            start: Starting index of insights to fetch (default: 0)
            end: Last index of insights to fetch (default: 100, max range: 100)
    
        Returns:
            Dictionary containing insights data and total count
        """
        auth = get_auth_instance()
        if auth is None:
            return {
                "status": "error",
                "error": "QuantConnect authentication not configured. Use configure_auth() first.",
            }
    
        # Validate range
        if end - start > 100:
            return {
                "status": "error",
                "error": "Range too large: end - start must be less than or equal to 100",
            }
    
        if start < 0 or end < 0:
            return {
                "status": "error",
                "error": "Start and end indices must be non-negative",
            }
    
        if start >= end:
            return {
                "status": "error",
                "error": "Start index must be less than end index",
            }
    
        try:
            # Prepare request data
            request_data = {
                "projectId": project_id,
                "backtestId": backtest_id,
                "start": start,
                "end": end,
            }
    
            # Make API request
            response = await auth.make_authenticated_request(
                endpoint="backtests/read/insights", method="POST", json=request_data
            )
    
            # Parse response
            if response.status_code == 200:
                data = response.json()
    
                if data.get("success", False):
                    insights = data.get("insights", [])
                    length = data.get("length", 0)
    
                    return {
                        "status": "success",
                        "project_id": project_id,
                        "backtest_id": backtest_id,
                        "start": start,
                        "end": end,
                        "insights": insights,
                        "length": length,
                        "message": f"Successfully retrieved {length} insights from backtest {backtest_id} (range: {start}-{end})",
                    }
                else:
                    # API returned success=false
                    errors = data.get("errors", ["Unknown error"])
                    return {
                        "status": "error",
                        "error": "Failed to read backtest insights",
                        "details": errors,
                        "project_id": project_id,
                        "backtest_id": backtest_id,
                    }
    
            elif response.status_code == 401:
                return {
                    "status": "error",
                    "error": "Authentication failed. Check your credentials and ensure they haven't expired.",
                }
    
            else:
                return {
                    "status": "error",
                    "error": f"API request failed with status {response.status_code}",
                    "response_text": (
                        response.text[:500]
                        if hasattr(response, "text")
                        else "No response text"
                    ),
                }
    
        except Exception as e:
            return {
                "status": "error",
                "error": f"Failed to read backtest insights: {str(e)}",
                "project_id": project_id,
                "backtest_id": backtest_id,
                "start": start,
                "end": end,
            }
  • Registration call that invokes register_backtest_tools(mcp), which defines and registers the read_backtest_insights tool (and other backtest tools) with the FastMCP server instance.
    register_backtest_tools(mcp)
  • Alternative entry point registration call that registers the backtest tools including read_backtest_insights.
    register_backtest_tools(mcp)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the tool 'reads' insights, implying a read-only operation, but doesn't disclose behavioral traits like authentication needs, rate limits, error conditions, or what 'insights data' entails. The description is minimal beyond stating the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose followed by structured 'Args' and 'Returns' sections. Every sentence earns its place, though it could be more concise by integrating the parameter details more seamlessly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (4 parameters, no annotations, but an output schema exists), the description is moderately complete. It covers the basic purpose and parameters but lacks context on usage, behavioral details, and what 'insights data' means, though the output schema mitigates the need to explain return values fully.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds value by explaining parameters in the 'Args' section, clarifying 'start' and 'end' as indices with defaults and a max range. However, it doesn't fully cover semantics like what 'insights' are or how indices relate to the data, leaving gaps given the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('read') and resource ('insights from a backtest'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'read_backtest' or 'read_live_insights', which would require more specific context about what makes backtest insights distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. With siblings like 'read_backtest' (likely for general backtest data) and 'read_live_insights' (for live algorithm insights), the description lacks context on when this specific tool is appropriate, offering only basic parameter info without usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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